Dynamic expression of hepatic GP73 mRNA and protein and circulating GP73 during hepatocytes malignant transformation.

BACKGROUND: Hepatic Golgi protein-73 (GP73) expression is related to hepatocellular carcinoma (HCC) progression. The aim of this study was to investigate the dynamic expression of GP73 mRNA and protein during hepatocytes malignant transformation. METHODS: Human GP73 expressions in 88 HCC tissues and their self-control surrounding tissues were examined by immunohistochemistry, and survival time of HCC patients was evaluated by the Kaplan-Meier method. HCC model of Sprague-Dawley rats was made by diet containing 2-fluorenylacetamide. The rats were divided into the control, hepatocyte degeneration, precanceration, and HCC groups to observe GP73 protein and mRNA alterations during hepatocytes malignant transformation. RESULTS: The GP73 expression was significantly higher in the cancerous tissues than that in the surrounding tissues, with shorter survival time, and the positive rates of GP73 protein in human HCC tissues were 53.3% at stage I, 84.0% at stage II, 84.6% at stage III, and 60.0% at stage IV, respectively. The positive rates of hepatic GP73 protein and mRNA in the rat models were none in the control group, 66.7% and 44.4% in the hepatocytes degeneration group, 88.9% and 77.8% in the hepatocytes precanceration group, and 100% in the HCC group, respectively. There was a positive correlation (r = 0.91, P<0.01) between hepatic GP73 and serum GP73 during rat hepatocytes malignant transformation. CONCLUSIONS: Abnormal GP73 expression may be a sensitive and valuable biomarker in hepatocarcinogensis.

Alteration of oncogenic IGF-II gene methylation status associates with hepatocyte malignant transformation.

BACKGROUND: Oncogenic insulin-like growth factor-II (IGF-II) is overexpressed in hepatocellular carcinoma (HCC). The present study aimed to analyze the dynamic alteration of IGF-II CpG site methylation status and its molecular mechanism in HCC progression. METHODS: IGF-II alterations were observed in rat hepatocarcinogenesis models induced by 2-acetylaminofluorene. Liver IGF-II expression was compared by immunohistochemistry or tissue IGF-II specific concentration (nmol/mg protein). Status of human IGF-II promoter 3 (P3) or rat IGF-II P2 CpG site methylation was amplified by methylation-specific polymerase chain reaction (MSP). Serum IGF-II levels were quantitatively detected by an enzyme-linked immunosorbent assay. RESULTS: The levels of hepatic IGF-II expression were significantly elevated in the HCC group (P < 0.001). The unmethylation rate of IGF-II P3 CpG sites was 100% in the HCC-, 52.5% in the paracancerous-, and none (0%) in the distal noncancerous-tissues. Abnormal IGF-II expression was related to differentiation degree, tumor invasion, and positive HBV-DNA (all P < 0.001), with a negative correlation between P3 methylation degree and IGF-II expression. There was a positive correlation between liver IGF-II specific concentration and circulating IGF-II level (r = 0.97, P < 0.001). Significantly negative correlation was found between IGF-II P2 CpG site methylation and circulating IGF-II (rs = -0.89, P < 0.001) or liver IGF-II level (rs = -0.84, P < 0.001). CONCLUSIONS: The increase of serum IGF-II and the alteration of oncogenic gene IGF-II methylation may be biomarkers for HCC diagnosis and DNA methylation may be the therapeutic target of HCC.

Association between XRCC1 gene polymorphisms and risk of glioma development: a meta-analysis.

OBJECTIVE: Previous studies of the association between X-ray cross-complementing group 1 (XRCC1) gene polymorphisms and the gliomas risk have yielded conflicting results, and thus a meta-analysis was performed to provide a more accurate estimation. METHODS: A computerized literature search of 5 electronic databases was conducted to identify the relevant studies. Fixed or random effect models were selected based on the heterogeneity test. Publication bias was estimated using Begg's funnel plots and Egger's regression test. RESULTS: A total of 11 studies (3,810 cases and 6,079 controls), 7 studies (2,928 cases and 5,048 controls), and 4 studies (1,461 cases and 2,593 controls) were finally included in the analyses of the association between XRCC1 Arg399Gln, Arg194Trp, and Arg280His polymorphisms and glioma risk, respectively. The pooled results showed that GlnGln carriage was associated with moderately increased risk of gliomas in Asians (GlnGln vs. ArgArg, OR=1.490, 95%CI 1.031-2.153; GlnGln/ArgGln vs. ArgArg, OR=1.321, 95%CI 1.037- 1.684), whereas a marginal association was revealed in Caucasians. For the Arg194Trp polymorphism, although a significant association was shown in the homozygous genotype comparisons (TrpTrp vs. ArgArg, OR = 2.209, 95%CI 1.398- 2.945), no significant link was found on subgroup analysis stratified by ethnicity. With regard to the Arg280His polymorphism, no significant association was found in each comparison. No particular study was found to significantly influence the pooled results, and no potential publication bias was detected. CONCLUSIONS: This meta-analysis suggested that the XRCC1 Arg399Gln polymorphism is moderately associated with increased risk of gliomas in Asians, while Arg194Trp and Arg280His polymorphisms demonstrated no significant influence. Due to the limited studies and the potential confounders, further studies are needed to confirm these results.

[FISH analysis of chromosomes of sweet potato(Ipomoea batatas cv.Xushu No.18)].

In order to understand the chromosome structure of sweet potato (Ipomoea batatas cv. Xushu 18), molecular cytogenetic analyses were carried out on I. batatas. by using 45S rDNA fluorescence in situ hybridization (45S rDNA-FISH), self genomic in situ hybridization (self-GISH), and silver staining techniques. Twelve, sixteen, and eighteen regions were silver stained in the interphase nucleus of I. batatas. The results of FISH analysis demonstrated 16 or 18 signals with different intensity on chromosomes of I. batatas. Self-GISH analysis showed that the intensive signals on I. batatas mitotic chromosomes were distributed along the chromosomes. However, the signals located in centromeric, subcentromeric, and telomeric regions were stronger and denser than those in other regions.

[Application of wavelet packet analysis in estimating soil parameters based on NIR spectra].

Using the method of wavelet analysis, the NIR spectra of soil samples were decomposed and reconstructed, and higher precision PLS models were established to estimate soil parameter (TN, SOM). One hundred fifty soil samples were collected from a winter wheat field and the NIR spectra of all samples were measured. Firstly, experiment statistic features were analyzed aiming at all soil samples, and the system clustering was carried out for TN and SOM respectively. Then 50 new TN samples and their corresponding spectra, and 50 new SOM samples and their corresponding spectra were obtained. Secondly, the PLS models were established with these new samples based on their corresponding spectra. The models showed a certain amount of accuracy, but it was still not practical. Therefore, wavelet analysis of NIR spectra was tried. The wavelet packet decomposing by eight-level biorthogonal algorithm was carried out, and 256 nodes were gotten. The lowest approximation signal is corresponding to soil moisture and soil texture spectrum trend. The maximal detail signal is corresponding to the high-frequency turbulence caused by the soil particle size, precision of spectrometer, and other uncertainties. After reconstructing these two nodes and then removed from the original spectra, the characteristic spectra corresponding to each soil parameter were acquired. Finally, the PLS models were established for TN and SOM content respectively: for TN content, the calibration coefficient of the PLS model is 0.960, the validation coefficient is 0.920; and for SOM content, the calibration coefficient of the PLS model is 0.922, and the validation coefficient is 0.883. It was showed that the accuracy of each model was highly improved and the models were able to meet the needs of actual production. The research results conclude that wavelet analysis can eliminate or substantially reduce the factors outside the parameters. It can also remove the obstacles in establishing linear models of soil parameters, and it is feasible and potential method for the real-time estimation of soil parameters.

[Estimation of soil organic matter and soil total nitrogen based on NIR spectroscopy and BP neural network].

Estimation models of soil organic matter (SOM) and soil total nitrogen (TN) were established based on NIR spectroscopy and BP neural network A total of 150 soil samples were collected from the tested farm, and the NIR spectra of all soil samples were measured. First, data pretreatment was performed for each sample with the method of locally weighted scatter plot smooth filtering. Then the box plot analysis for the measured SOM data and TN data were conducted separately and the information about the shape, location, and distribution of the target data was obtained. The variance between the SOM data was very small, and most of them were concentrated on the median. This was also observed from TN data. Thus clustering analysis was carried out for the target parameters of the soil samples so that the original dataset with 150 spectra was clustered to 50 groups. For each group, the average of spectral data was calculated at every wavelength to obtain a new spectrum. The new spectrum was calculated with natural logarithm and normalized, which was taken as a new sample. Principal component analysis (PCA) was executed for 50 new samples and the principal components with over 99.98% of cumulative proportion of correlation matrix were extracted to establish BP neural network. According to the analysis result of SOM content, the calibration accuracy of the model was 0.999, and the validation accuracy reached to 0.854. According to the analysis result of the soil TN content, the calibration accuracy of the model was close to 1, and the validation accuracy reached 0.808. The result shows that the smooth filter can weaken the noise in the data, expose the data features, provide a reasonable starting approach for parametric fitting; and improve the prediction accuracy; It is feasible and practical to estimate soil parameters by using BP neural network with the prediction accuracy of 0.854 (SOM) and 0.808 (TN); Compared to the other prediction modeling method, the BP neural network model has higher robustness and better fault tolerance, and the model accuracy would not be affected by the several outline samples when the number of samples is large enough.

[Development of soil-organic-matter fast-determination instrument based on spectroscopy].

An instrument used to measure the content of soil organic matter was developed based on spectroscopy. The instrument consists of an optical unit and an electronic unit. The optical unit is used to collect the reflecting light from soil surface and change the filtered light into the electrical signal. The electronic unit includes an amplifying circuit, an A/D circuit, and a display and storage circuit. Three wavebands of filters, 530, 765, and 850 nm, were selected in the performance test in order to determine the optimal one. The soil samples tested include two types, dried soil and air-dry soil. The result shows that there is highest correlation between the reflectance and the content of soil organic matter at the wavelength of 850 nm (r2 = 0.930) for dried soil. While the coefficient of determination r2 is abouu 0.711 for air-dry soil at the same wavelength. Although the actual precision is lower because of the soil moisture, it is practical in real-time measurement in the field.

[New method and instrument to diagnose crop growth status in greenhouse based on spectroscopy].

Spectral reflectance of cucumber leaves in greenhouse was measured using an ASD FieldSpec Pro VNIR spectrometer with natural illumination. Two sensitive wavelengths, 527 nm and 762 nm, were selected to evaluate the nitrogen content of the cucumber leaves. A model was established and validated using normal difference color index(NDCI) with the correlation coefficient of 0.881. Based on the above efforts, a handheld spectral instrument was developed to diagnose the growth status of the crop in greenhouse using fiber optics. The instrument was mainly composed of four parts: reflected light acquisition system, light intensity measurement unit, signal conditioning unit, and data acquisition system. The sunlight reflected by the crop was transmitted by the fiber, and passed through the light filter to obtain light at the sensitive wavelengths. Finally it was transformed into electronic signal by the photoelectric transistor, and was used to diagnose the growth status of the crop according to the evaluation model. The result showed that the developed instrument was practical.

[Real-time analysis of soil moisture, soil organic matter, and soil total nitrogen with NIR spectra].

The grey-brown alluvial soil in northern China was selected as research object, and the feasibility and possibility of real-time analyzing soil para-fueter with NIR spectroscopic techniques were explored. One hundred fifty samples were collected from a winter wheat farm. NIR absorbance spectra were rapidly measured under their original conditions by a Nicolet Antaris FT-NIR analyzer. Three soil parameters, namely soil moisture, SOM (soil organic matter) and TN (total nitrogen) content, were analyzed. For soil moisture content, a linear regression model was available, using 1920 nm wavelength with correlation coefficient of 0.937, so that the results obtained could be directly used to real-time evaluate soil moisture. SOM content and TN content were estimated with a muviaiple linear regression model, 1870 and 1378 nm wavelengths were selected in the SOM estimate model, and 2262 and 1888 nrameter wavelengths were selected in the TN estimate model. The results showed that soil SOM and TN contents can be evaluated by using NIR absorbance spectra of soil samples.